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423 results on '"Podgorelec, Vili"'

1. Profiling the carbon footprint of performance bugs

Author

Fister Jr., Iztok, Fister, Dušan, Podgorelec, Vili, and Fister, Iztok

Subjects
Computer Science - Software Engineering
Abstract

Much debate nowadays is devoted to the impacts of modern information and communication technology on global carbon emissions. Green information and communication technology is a paradigm creating a sustainable and environmentally friendly computing field that tries to minimize the adverse effects on the environment. Green information and communication technology are under constant development nowadays. Thus, in this paper, we undertake the problem of performance bugs that, until recently, have never been studied so profoundly. We assume that inappropriate software implementations can have a crucial influence on global carbon emissions. Here, we classify those performance bugs and develop inappropriate implementations of four programs written in C++. To mitigate these simulated performance bugs, measuring software and hardware methods that can estimate the increased carbon footprint properly were proposed.

Published
2024

2. A comprehensive review of visualization methods for association rule mining: Taxonomy, Challenges, Open problems and Future ideas

Author

Fister Jr., Iztok, Fister, Iztok, Fister, Dušan, Podgorelec, Vili, and Salcedo-Sanz, Sancho

Subjects
Computer Science - Databases, Computer Science - Artificial Intelligence
Abstract

Association rule mining is intended for searching for the relationships between attributes in transaction databases. The whole process of rule discovery is very complex, and involves pre-processing techniques, a rule mining step, and post-processing, in which visualization is carried out. Visualization of discovered association rules is an essential step within the whole association rule mining pipeline, to enhance the understanding of users on the results of rule mining. Several association rule mining and visualization methods have been developed during the past decades. This review paper aims to create a literature review, identify the main techniques published in peer-reviewed literature, examine each method's main features, and present the main applications in the field. Defining the future steps of this research area is another goal of this review paper.

Published
2023

3. Time series numerical association rule mining variants in smart agriculture

Author

Fister Jr., Iztok, Fister, Dušan, Fister, Iztok, Podgorelec, Vili, and Salcedo-Sanz, Sancho

Subjects
Computer Science - Neural and Evolutionary Computing
Abstract

Numerical association rule mining offers a very efficient way of mining association rules, where algorithms can operate directly with categorical and numerical attributes. These methods are suitable for mining different transaction databases, where data are entered sequentially. However, little attention has been paid to the time series numerical association rule mining, which offers a new technique for extracting association rules from time series data. This paper presents a new algorithmic method for time series numerical association rule mining and its application in smart agriculture. We offer a concept of a hardware environment for monitoring plant parameters and a novel data mining method with practical experiments. The practical experiments showed the method's potential and opened the door for further extension.

Published
2022

4. Aligning Learners' Expectations and Performance by Learning Analytics Systemwith a Predictive Model

Author

Brdnik, Saša, Šumak, Boštjan, and Podgorelec, Vili

Subjects
Computer Science - Human-Computer Interaction, H.5
Abstract

Learning analytics (LA) is data collection, analysis, and representation of data about learners in order to improve their learning and performance. Furthermore, LA opens the door to opportunities for self-regulated learning in higher education, a circular process in which learners activate and sustain behaviours that are oriented toward their personal learning goals. The potentials of LA and self-regulated learning are huge; however, they are not yet widely applied in higher education institutions. Slovenian higher education institutions have lagged behind other European countries in LA adoption. Our research aims to fill this gap by using a qualitatively and quantitatively led workflow for building a requirement-oriented LA solution, consisting of empirically gathering the students' expectations of LA and presenting a dashboard solution. Translated Student Expectations of Learning Analytics Questionnaire and focus groups were used to gather expectations from learners. Based on them, a user interface utilizing LA and grade prediction with an AI model was implemented for a selected course. The interface includes early grade prediction, peer comparison, and historical data overview. Grade prediction is based on a machine learning model built on users' interaction in the virtual learning environment, demographic data and lab grades. First, classification is used to determine students at risk of failing - its precision reaches 98% after the first month of the course. Second, the exact grade is predicted with the Decision Tree Regressor, reaching a mean absolute error of 11.2grade points (on a 100 points scale) after the first month. The proposed system's main benefit is the support for self-regulation of the learning process during the semester, possibly motivating students to adjust their learning strategies to prevent failing the course. Initial student evaluation showed positive results., Comment: 11 pages, 4 figures, submitted to ACM IUI conference

Published
2022

6. Machine Learning Model for Student Drop-Out Prediction Based on Student Engagement

Author

Brezočnik, Lucija, Nalli, Giacomo, De Leone, Renato, Val, Sonia, Podgorelec, Vili, Karakatič, Sašo, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Karabegovic, Isak, editor, Kovačević, Ahmed, editor, and Mandzuka, Sadko, editor

Published
2023
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9. Digital Transformation Using Artificial Intelligence and Machine Learning: An Electrical Energy Consumption Case

Author

Podgorelec, Vili, Karakatič, Sašo, Fister, Iztok, Jr., Brezočnik, Lucija, Pečnik, Špela, Vrbančič, Grega, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Karabegović, Isak, editor, Kovačević, Ahmed, editor, and Mandžuka, Sadko, editor

Published
2022
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10. A Strategy for Applying Open Data Initiatives

Author

Brezočnik, Lucija, Polančič, Gregor, Karakatič, Sašo, Vrbančič, Grega, Pečnik, Špela, Podgorelec, Vili, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Karabegović, Isak, editor, Kovačević, Ahmed, editor, and Mandžuka, Sadko, editor

Published
2022
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11. Synthetic Data Augmentation of Cycling Sport Training Datasets

Author

Fister, Iztok, Vrbančič, Grega, Podgorelec, Vili, Fister, Iztok, Jr., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Vasant, Pandian, editor, Zelinka, Ivan, editor, and Weber, Gerhard-Wilhelm, editor

Published
2022
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12. Recent Applications of Explainable AI (XAI): A Systematic Literature Review.

Author

Saarela, Mirka and Podgorelec, Vili

Subjects
CONVOLUTIONAL neural networks, ARTIFICIAL intelligence, MACHINE learning, DEEP learning, WEB databases
Abstract

This systematic literature review employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to investigate recent applications of explainable AI (XAI) over the past three years. From an initial pool of 664 articles identified through the Web of Science database, 512 peer-reviewed journal articles met the inclusion criteria—namely, being recent, high-quality XAI application articles published in English—and were analyzed in detail. Both qualitative and quantitative statistical techniques were used to analyze the identified articles: qualitatively by summarizing the characteristics of the included studies based on predefined codes, and quantitatively through statistical analysis of the data. These articles were categorized according to their application domains, techniques, and evaluation methods. Health-related applications were particularly prevalent, with a strong focus on cancer diagnosis, COVID-19 management, and medical imaging. Other significant areas of application included environmental and agricultural management, industrial optimization, cybersecurity, finance, transportation, and entertainment. Additionally, emerging applications in law, education, and social care highlight XAI's expanding impact. The review reveals a predominant use of local explanation methods, particularly SHAP and LIME, with SHAP being favored for its stability and mathematical guarantees. However, a critical gap in the evaluation of XAI results is identified, as most studies rely on anecdotal evidence or expert opinion rather than robust quantitative metrics. This underscores the urgent need for standardized evaluation frameworks to ensure the reliability and effectiveness of XAI applications. Future research should focus on developing comprehensive evaluation standards and improving the interpretability and stability of explanations. These advancements are essential for addressing the diverse demands of various application domains while ensuring trust and transparency in AI systems. [ABSTRACT FROM AUTHOR]

Published
2024
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13. Improved Nature-Inspired Algorithms for Numeric Association Rule Mining

Author

Fister Jr., Iztok, Podgorelec, Vili, Fister, Iztok, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Vasant, Pandian, editor, Zelinka, Ivan, editor, and Weber, Gerhard-Wilhelm, editor

Published
2021
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15. Nature-Inspired Cryptoanalysis Methods for Breaking Vigenère Cipher

Author

Brezočnik, Lucija, Fister, Iztok, Jr., Podgorelec, Vili, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, and Karabegović, Isak, editor

Published
2020
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16. Solving Agile Software Development Problems with Swarm Intelligence Algorithms

Author

Brezočnik, Lucija, Fister, Iztok, Jr., Podgorelec, Vili, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, and Karabegović, Isak, editor

Published
2020
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17. On the Potential of the Nature-Inspired Algorithms for Pure Binary Classification

Author

Fister, Iztok, Jr., Fister, Iztok, Fister, Dušan, Vrbančič, Grega, Podgorelec, Vili, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Krzhizhanovskaya, Valeria V., editor, Závodszky, Gábor, editor, Lees, Michael H., editor, Dongarra, Jack J., editor, Sloot, Peter M. A., editor, Brissos, Sérgio, editor, and Teixeira, João, editor

Published
2020
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21. Using Machine Learning and Natural Language Processing for Unveiling Similarities between Microbial Data.

Author

Brezočnik, Lucija, Žlender, Tanja, Rupnik, Maja, and Podgorelec, Vili

Subjects
NATURAL language processing, CATTLE manure, POLLUTANTS, HYPERVARIABLE regions, HIERARCHICAL clustering (Cluster analysis)
Abstract

Microbiota analysis can provide valuable insights in various fields, including diet and nutrition, understanding health and disease, and in environmental contexts, such as understanding the role of microorganisms in different ecosystems. Based on the results, we can provide targeted therapies, personalized medicine, or detect environmental contaminants. In our research, we examined the gut microbiota of 16 animal taxa, including humans, as well as the microbiota of cattle and pig manure, where we focused on 16S rRNA V3-V4 hypervariable regions. Analyzing these regions is common in microbiome studies but can be challenging since the results are high-dimensional. Thus, we utilized machine learning techniques and demonstrated their applicability in processing microbial sequence data. Moreover, we showed that techniques commonly employed in natural language processing can be adapted for analyzing microbial text vectors. We obtained the latter through frequency analyses and utilized the proposed hierarchical clustering method over them. All steps in this study were gathered in a proposed microbial sequence data processing pipeline. The results demonstrate that we not only found similarities between samples but also sorted groups' samples into semantically related clusters. We also tested our method against other known algorithms like the Kmeans and Spectral Clustering algorithms using clustering evaluation metrics. The results demonstrate the superiority of the proposed method over them. Moreover, the proposed microbial sequence data pipeline can be utilized for different types of microbiota, such as oral, gut, and skin, demonstrating its reusability and robustness. [ABSTRACT FROM AUTHOR]

Published
2024
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25. Scrum Task Allocation Based on Particle Swarm Optimization

Author

Brezočnik, Lucija, Fister, Iztok, Jr., Podgorelec, Vili, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Korošec, Peter, editor, Melab, Nouredine, editor, and Talbi, El-Ghazali, editor

Published
2018
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31. NarmViz: A novel method for visualization of time series numerical association rules for smart agriculture.

Author

Fister, Iztok, Podgorelec, Vili, Salcedo‐Sanz, Sancho, and Holzinger, Andreas

Subjects
*ASSOCIATION rule mining, *DATA visualization, *ARTIFICIAL intelligence, *DATA mining, *TIME series analysis
Abstract

Numerical association rule mining (NARM) is a popular method under the umbrella of data mining, focused on finding relationships between attributes in transaction databases. Numerical association rules for time series are a new paradigm that extends the applicability of NARM to the domain of time series. Association rule mining algorithms result in numerous rules, the interpretation of which is sometimes not easy for human experts. Therefore, various visualization methods have been developed to improve the explanation results of the rule mining process. This article is a novel contribution to the development of a new visualization method capable of presenting the association rules for time series developed according to the principles of explainable artificial intelligence. The experiments are conducted in the context of smart agriculture (i.e., agricultural time series data), and show the great potential of the proposed visualization method for the future. [ABSTRACT FROM AUTHOR]

Published
2024
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34. An Analysis of the Content of Knowledge Management-Related Courses

Author

Gradišnik, Mitja, Podgorelec, Vili, Heričko, Marjan, van der Aalst, Wil, Series editor, Mylopoulos, John, Series editor, Rosemann, Michael, Series editor, Shaw, Michael J., Series editor, Szyperski, Clemens, Series editor, Uden, Lorna, editor, Heričko, Marjan, editor, and Ting, I-Hsien, editor

Published
2015
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38. Predicting Grades Based on Students’ Online Course Activities

Author

Černezel, Aleš, Karakatič, Sašo, Brumen, Boštjan, Podgorelec, Vili, van der Aalst, Wil, Series editor, Mylopoulos, John, Series editor, Rosemann, Michael, Series editor, Shaw, Michael J., Series editor, Szyperski, Clemens, Series editor, Uden, Lorna, editor, Fuenzaliza Oshee, Darcy, editor, Ting, I-Hsien, editor, and Liberona, Dario, editor

Published
2014
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42. Ontology and Rule Based Inferring on Project Teams

Author

Podgorelec, Vili, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Cipolla-Ficarra, Francisco, editor, Veltman, Kim, editor, Verber, Domen, editor, Cipolla-Ficarra, Miguel, editor, and Kammüller, Florian, editor

Published
2012
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43. Combining Semantic Web Technologies and Rule-Based Systems for Building Advanced Medical Applications

Author

Podgorelec, Vili, Gradišnik, Mitja, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Cipolla-Ficarra, Francisco, editor, Veltman, Kim, editor, Verber, Domen, editor, Cipolla-Ficarra, Miguel, editor, and Kammüller, Florian, editor

Published
2012
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45. Expert-Assisted Classification Rules Extraction Algorithm

Author

Podgorelec, Vili, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Catania, Barbara, editor, Ivanović, Mirjana, editor, and Thalheim, Bernhard, editor

Published
2010
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50. Notes on Medical Decision Model Creation

Author

Šprogar, Matej, Kokol, Peter, Zorman, Milan, Podgorelec, Vili, Lhotska, Lenka, Klema, Jiří, Goos, Gerhard, editor, Hartmanis, Juris, editor, van Leeuwen, Jan, editor, Crespo, Jose, editor, Maojo, Victor, editor, and Martin, Fernando, editor

Published
2001
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